Generalizability of REDUCE-IT eligibility criteria in a large diabetes cardiovascular outcomes trial: A post hoc subgroup analysis of EMPA-REG outcome
Bibliographic record
Abstract
REDUCE-IT showed that icosapent ethyl (IPE) improved cardiovascular (CV) outcomes in participants with established CV disease (CVD) or type 2 diabetes (T2D) and at least one additional risk factor plus mild-moderate hypertriglyceridemia and reasonably controlled low-density lipoprotein cholesterol (LDL-C). As the generalizability of REDUCE-IT has not been investigated in a T2D population with established CVD, this post hoc analysis investigated how many participants from EMPA-REG OUTCOME, which tested the effects of empagliflozin versus placebo on CV outcomes in participants with T2D and CVD, would have been eligible for IPE treatment, and whether CV outcomes differed based on eligibility for IPE treatment. Participants from EMPA-REG OUTCOME were screened for inclusion using both REDUCE-IT-like criteria (baseline statin therapy, triglycerides 135–499 mg/dL and LDL-C 41–100 mg/dL) and slightly amended FDA indication criteria (triglycerides ≥150 mg/dL). Analyses were conducted to characterize the study population and CV outcomes in participants eligible for IPE versus those not eligible for IPE. Of the 7020 participants from EMPA-REG OUTCOME, 1810 (25.8%) fulfilled REDUCE-IT criteria and 3182 (45.3%) fulfilled FDA criteria for IPE treatment. Treatment effects of empagliflozin versus placebo on CV and kidney outcomes and mortality were consistent in participants meeting REDUCE-IT and FDA criteria and those who did not. These results indicate that a sizable proportion of patients with diabetes and established CVD, such as those in EMPA-REG OUTCOME, may be eligible for IPE treatment to lower residual CV risk. Treatment benefit with empagliflozin was consistent, regardless of REDUCE-IT or FDA eligibility criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".